Real experiences from people who took the time to share what they found useful, what was challenging, and what they walked away with.
Back to HomeA selection of feedback from people who have completed or are progressing through our programmes.
"I had tried a couple of video-based Python courses before and always stalled about three weeks in. What made this different was the weekly session — having to show up and bring actual questions kept me moving. By Week 6 I had written my first small data pipeline and it actually worked on a real CSV I pulled from a government dataset."
"The code reviews were the most valuable part for me. I could get code to run, but I did not always know if I was doing it in a way that made sense at scale. The feedback I got on my Week 9 project changed how I think about data preprocessing. I came in knowing basic Python and left with a portfolio piece I am genuinely proud to explain."
"I work full time in HR and was nervous about whether I could keep up. The pacing turned out to be very manageable — about eight to nine hours a week including the live session. The mentors were patient with questions that probably felt obvious to them, and the portfolio project was something I could actually explain to people who asked what I had been studying."
"The capstone project was harder than I expected — deliberately so, I think. The one-to-one sessions during that period were where I got the most out of the programme. My mentor pushed back on some of my architectural decisions in a way that made me rethink the whole thing. The final project is something I have walked through in two technical conversations since finishing, and it holds up."
"I appreciated that they were straightforward about what the programme does and does not promise. They never oversold anything. The small group size meant I could see how other people approached the same problems differently, and that was unexpectedly useful. One thing I would flag: the pace picks up noticeably around Weeks 8-9, so come in ready to put in a bit more time during that stretch."
"The section on deployment and responsible practices was different from anything I had seen in other resources. It was not just ethical theory — it was about actual engineering decisions, like how to handle model drift and what to document when you put something in production. I did not expect that to be one of the more practically useful parts of the programme, but it was."
Where people started, what they worked through, and where they landed.
AI Foundations → ML Engineering Track
Farid had spent several years in office administration and had no programming background whatsoever. He had been following news about AI and wanted to understand what was actually happening behind the terminology, not just read articles about it.
He joined the AI Foundations Programme in late 2024, taking things slowly in the first few weeks while Python felt unfamiliar. By the midpoint he was completing exercises ahead of time. He moved into the ML Engineering Track three months later with a much stronger footing than he expected going in.
Farid's ML Engineering portfolio project involved building a classification model on a real public dataset. He documented the process thoroughly and can walk through it clearly. He says the biggest change is that he now reads technical writing about AI and understands the majority of it without translation.
ML Engineering Track → Advanced AI
Rachel worked as a junior web developer and had Python experience from university. She wanted to move into work involving data and models but felt her self-study had given her scattered knowledge with no real depth in any area.
She joined at the ML Engineering Track level, skipping Foundations. The code reviews revealed gaps she had not noticed in her self-study — particularly around how she was handling train/test splits and what her evaluation metrics were actually telling her. She found that uncomfortable but useful.
Rachel is currently midway through the Advanced programme. Her capstone project involves fine-tuning a model on a domain-specific dataset and documenting the deployment decisions she would make in a real context. She says the one-to-one sessions have been more challenging than she expected — in a way she finds helpful.
AI Foundations Programme
Nizam had an engineering degree but had been in a management role for over a decade. He was used to learning new things, but formal study felt distant and he was not sure whether an online programme would hold his attention.
He found the first two weeks harder than expected — not because of the Python specifically, but because study habits take time to rebuild. The live sessions helped because there was a structure to the week he had not had before. His mentor was direct about where his code was unclear, which he appreciated.
He completed the Foundations programme in early June 2025. His portfolio project involved using a public dataset of Malaysian traffic incident reports to build a simple classifier. He says the main thing he took from the eight weeks was a much clearer sense of what ML actually involves and a more realistic picture of the field.
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